Performance comparison of deep learning models for MRI-based brain tumor detection

Abdulmajeed Alsufyani; Abdulmajeed Alsufyani

doi:10.3934/bioeng.2025001

AIMS Bioengineering

2025, Volume 12, Issue 1: 1-21. doi: 10.3934/bioeng.2025001

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Performance comparison of deep learning models for MRI-based brain tumor detection

Abdulmajeed Alsufyani ^,

Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

Received: 02 November 2024 Revised: 27 December 2024 Accepted: 02 January 2025 Published: 15 January 2025

Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.
- deep learning,
- brain tumor detection,
- YOLOv9
Citation: Abdulmajeed Alsufyani. Performance comparison of deep learning models for MRI-based brain tumor detection[J]. AIMS Bioengineering, 2025, 12(1): 1-21. doi: 10.3934/bioeng.2025001

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Abstract

Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.

https://www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tumor-detection/data

Conflict of interest

The author declares no conflict of interest.

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